Optimization Strategies for A/B Testing on HADOOP
Summary: Resource-aware optimization for concurrent Hive/MapReduce on Hadoop; data-dependency inter-query optimization and cluster-load modeling. First to optimize A/B testing on Hadoop; 233% speedup for concurrent vs sequential; up to 40% extra with resource tuning. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Andrii Cherniak
- 2. Huma Zaidi
- 3. Vladimir Zadorozhny
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 588 | Practical Skew Handling in Parallel Joins | 1992 | VLDB | 0.00019604754 |
| 868 | Profiling, What-if Analysis, and Cost-based Optimization of MapReduce Programs | 2011 | VLDB | 0.00015789681 |
| 1,071 | Starfish: A Self-tuning System for Big Data Analytics | 2011 | CIDR | 0.00014312777 |
| 3,601 | Large-Scale Machine Learning at Twitter | 2012 | SIGMOD | 6.9315087e-05 |
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